Systems and methods for providing traffic light region of interest parameters to an autonomous vehicle

ABSTRACT

A sequence of images and a vehicle location associated with each of the images is received at a traffic light ROI management system. At least one traffic light is detected in each image. A ECS traffic light ROI is defined for each image. The ECS traffic light ROI encloses the detected traffic lights. A visual feature template is generated for each image. The visual feature template is based on the ECS traffic light ROI for the image. Each visual feature template is mapped to the vehicle location associated with the image to a HD map. The HD map is transmitted to an autonomous vehicle to enable the autonomous vehicle to identify a real-time traffic light ROI in a real-time image based on a match between a first visual feature template and real-time visual features of the real-time image at the vehicle location associated with the first visual feature template.

INTRODUCTION

The technical field generally relates to autonomous vehicles, and moreparticularly relates to systems and methods for generating a region ofinterest for traffic light detection.

The automated driving systems (ADS) of autonomous vehicles often rely onimages captured by vehicle cameras to detect traffic lights on roadsegments. The images captured by the vehicle cameras may be highresolution images, such as for example, 8-megapixel images. The use ofon-board computing resources at the autonomous vehicle to implementobject detection algorithms to detect traffic lights in the images mayresult in increased computer resource usage and traffic light detectionlatencies.

In some cases, a road segment leading to a traffic light may includecomplex road topologies, such as for example, bumps, curves, or hills.Compensating for complex road topologies to enable accurate detection oftraffic lights may place additional strain on the computational resourceat the autonomous vehicle.

SUMMARY

In an embodiment, an edge computing system (ECS) for providing trafficlight region of interest (ROI) parameters to an autonomous vehicleincludes a processor and a memory. The memory includes instructions thatupon execution by the processor cause the processor to receive asequence of images and a vehicle location associated with each of theimages from a first autonomous vehicle; detect at least one trafficlight in each image in the sequence of images; define a ECS trafficlight ROI for each image in the sequence of images, the ECS trafficlight ROI for each image enclosing the at least one traffic light in theimage; generate a visual feature template for each image in the sequenceof images, the visual feature template for each image being based on theECS traffic light ROI for the image; map each visual feature templatefor each image in the sequence of images to the vehicle locationassociated with the image to a high definition (HD) map; and transmitthe HD map to a second autonomous vehicle to enable the secondautonomous vehicle to identify a real-time traffic light ROI in areal-time image based on a match between a first visual feature templateof the HD map and real-time visual features associated with thereal-time image at the vehicle location associated with the first visualfeature template.

In an embodiment, each of the images in the sequence of images is in aforward time order and the memory further includes instructions thatupon execution by the processor cause the processor to define the ECStraffic light ROI in each image in the sequence of images in a reversetime order.

In an embodiment, the memory further includes instructions that uponexecution by the processor cause the processor to discard a first imageof the sequence of images upon a determination that the at least onedetected traffic light in first image falls below a traffic light sizethreshold.

In an embodiment, the memory further includes instructions that uponexecution by the processor cause the processor to generate the visualfeature template for each image in the sequence of images in thefrequency domain by applying a Fast Fourier transform to the ECS trafficlight ROI of the image.

In an embodiment, the memory further includes instructions that uponexecution by the processor cause the processor to select a second visualfeature template based on a first image of the sequence of images, thefirst image being associated with a first vehicle location; determinewhether the second visual feature template can be used to identify afirst ECS traffic light ROI in a second image of the sequence of imagesbased on a match between the second visual feature template and visualfeatures associated with the second image, the second image beingassociated with a second vehicle location; and map the second visualfeature template to the first and second vehicle locations on the HD mapbased on the determination.

In an embodiment, the memory further includes instructions that uponexecution by the processor cause the processor to determine ROI offsetsof the ECS traffic light ROI in consecutive pairs of images in thesequence of images based on a movement of the ECS traffic light ROI inthe consecutive pairs of images; generate a ROI scaling ratio for eachof the vehicle locations associated with each of the images in thesequence of images based a relationship between each of the ROI offsetsand the vehicle location associated with the ROI offset; and map eachROI scaling ratio to the vehicle location on the HD map associated withthe ROI scaling ratio.

In an embodiment, the memory further includes instructions that uponexecution by the processor cause the processor to transmit the HD mapincluding the mapped ROI scaling ratios to the second autonomous vehicleto enable the second autonomous vehicle to adjust a size of the realtime traffic light ROI in the real-time image in accordance with the ROIscaling ratio associated with the vehicle location of the secondautonomous vehicle.

In an embodiment, a computer readable medium includes instructionsstored thereon for providing traffic light region of interest (ROI)parameters to an autonomous vehicle, that upon execution by a processor,cause the processor to receive a sequence of images and a vehiclelocation associated with each of the images from a first autonomousvehicle; detect at least one traffic light in each image in the sequenceof images; define a ECS traffic light ROI for each image in the sequenceof images, the ECS traffic light ROI for each image enclosing the atleast one traffic light in the image; generate a visual feature templatefor each image in the sequence of images, the visual feature templatefor each image being based on the ECS traffic light ROI for the image;map each visual feature template for each image in the sequence ofimages to the vehicle location associated with the image to a highdefinition (HD) map; and transmit the HD map to a second autonomousvehicle to enable the second autonomous vehicle to identify a real-timetraffic light ROI in a real-time image based on a match between a firstvisual feature template of the HD map and real-time visual featuresassociated with the real-time image at the vehicle location associatedwith the first visual feature template.

In an embodiment, the computer readable medium further includesinstructions to cause the processor to receive the sequence of images ina forward time order; and define the ECS traffic light ROI in each imagein the sequence of images in a reverse time order.

In an embodiment, the computer readable medium further includesinstructions to cause the processor to discard a first image of thesequence of images upon a determination that the at least one detectedtraffic light in first image falls below a traffic light size threshold.

In an embodiment, the computer readable medium further includesinstructions to cause the processor to generate the visual featuretemplate for each image in the sequence of images in the frequencydomain by applying a Fast Fourier transform to the ECS traffic light ROIof the image.

In an embodiment, the computer readable medium further includesinstructions to cause the processor to select a second visual featuretemplate based on a first image of the sequence of images, the firstimage being associated with a first vehicle location; determine whetherthe second visual feature template can be used to identify a first ECStraffic light ROI in a second image of the sequence of images based on amatch between the second visual feature template and visual featuresassociated with the second image, the second image being associated witha second vehicle location; and map the second visual feature template tothe first and second vehicle locations on the HD map based on thedetermination.

In an embodiment, the computer readable medium further includesinstructions to cause the processor to determine ROI offsets of the ECStraffic light ROI in consecutive pairs of images in the sequence ofimages based on a movement of the ECS traffic light ROI in theconsecutive pairs of images; generate a ROI scaling ratio for each ofthe vehicle locations associated with each of the images in the sequenceof images based a relationship between each of the ROI offsets and thevehicle location associated with the ROI offset; and map each ROIscaling ratio to the vehicle location on the HD map associated with theROI scaling ratio.

In an embodiment, the computer readable medium further includesinstructions to cause the processor to transmit the HD map including themapped ROI scaling ratios to the second autonomous vehicle to enable thesecond autonomous vehicle to adjust a size of the real time trafficlight ROI in the real-time image in accordance with the ROI scalingratio associated with the vehicle location of the second autonomousvehicle.

In an embodiment, a method of providing traffic light region of interest(ROI) parameters to an autonomous vehicle includes receiving a sequenceof images and a vehicle location associated with each of the images froma first autonomous vehicle at a traffic light ROI management system;detecting at least one traffic light in each image in the sequence ofimages at the traffic light ROI management system; defining a ECStraffic light ROI for each image in the sequence of images at thetraffic light ROI management system, the ECS traffic light ROI for eachimage enclosing the at least one traffic light in the image; generatinga visual feature template for each image in the sequence of images atthe traffic light ROI management system, the visual feature template foreach image being based on the ECS traffic light ROI for the image;mapping each visual feature template for each image in the sequence ofimages to the vehicle location associated with the image to a highdefinition (HD) map at the traffic light ROI management system; andtransmitting the HD map from the traffic light ROI management system toa second autonomous vehicle to enable the second autonomous vehicle toidentify a real-time traffic light ROI in a real-time image based on amatch between a first visual feature template of the HD map andreal-time visual features associated with the real-time image at thevehicle location associated with the first visual feature template.

In an embodiment, the method further includes receiving the sequence ofimages in a forward time order at the traffic light ROI managementsystem; and defining the ECS traffic light ROI in each image in thesequence of images in a reverse time order at the traffic light ROImanagement system.

In an embodiment, the method further includes discarding a first imageof the sequence of images upon a determination that the at least onedetected traffic light in first image falls below a traffic light sizethreshold at the traffic light ROI management system.

In an embodiment, the method further includes generating the visualfeature template for each image in the sequence of images in thefrequency domain by applying a Fast Fourier transform to the ECS trafficlight ROI of the image at the traffic light ROI management system.

In an embodiment, the method further includes selecting a second visualfeature template based on a first image of the sequence of images at thetraffic light ROI management system, the first image being associatedwith a first vehicle location; determining whether the second visualfeature template can be used to identify a first ECS traffic light ROIin a second image of the sequence of images based on a match between thesecond visual feature template and visual features associated with thesecond image at the traffic light ROI management system, the secondimage being associated with a second vehicle location; and mapping thesecond visual feature template to the first and second vehicle locationson the HD map based on the determination at the traffic light ROImanagement system.

In an embodiment, the method further includes determining ROI offsets ofthe ECS traffic light ROI in consecutive pairs of images in the sequenceof images based on a movement of the ECS traffic light ROI in theconsecutive pairs of images at the traffic light ROI management system;generating a ROI scaling ratio for each of the vehicle locationsassociated with each of the images in the sequence of images based arelationship between each of the ROI offsets and the vehicle locationassociated with the ROI offset at the traffic light ROI managementsystem; and mapping each ROI scaling ratio to the vehicle location onthe HD map associated with the ROI scaling ratio at the traffic lightROI management system.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments will hereinafter be described in conjunction withthe following drawing figures, wherein like numerals denote likeelements.

FIG. 1 is a functional block diagram representation of an autonomousvehicle communicatively coupled to an edge computing system (ECS)including an embodiment of a traffic light region of interest (ROI)management system;

FIG. 2 is a diagrammatic representation of an autonomous vehiclecapturing a sequence of images including traffic lights for transmissionto an ECS including an embodiment of a traffic light ROI managementsystem;

FIG. 3 is a diagrammatic representation of a plurality of autonomousvehicles communicatively coupled to an ECS including an embodiment of atraffic light ROI management system;

FIG. 4 is a functional block diagram representation of an ECS includingan embodiment of a traffic light ROI management system;

FIG. 5 is a flowchart representation of an example of a method ofgenerating a plurality of ECS traffic light ROI based on a sequence ofimages received from an autonomous vehicle using an embodiment of atraffic light ROI management system;

FIG. 6 a and FIG. 6 b are block diagram representations of examples ofECS traffic light ROI positions in a pair of consecutive images in thesequence of images;

FIG. 7 is a diagrammatic representation of an example of associationsbetween each of plurality of visual feature templates and a plurality ofvehicle locations on a road segment including traffic lights;

FIG. 8 is a diagrammatic representation of an example of a vehiclelocation of an autonomous vehicle and vehicle locations associated witha plurality of visual feature templates on a road segment includingtraffic lights; and

FIG. 9 is a flowchart representation of a method of providing trafficlight ROI parameters to an autonomous vehicle.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding introduction, summary or the following detaileddescription. As used herein, the term module refers to any hardware,software, firmware, electronic control component, processing logic,and/or processor device, individually or in any combination, includingwithout limitation: application specific integrated circuit (ASIC), anelectronic circuit, a processor (shared, dedicated, or group) and memorythat executes one or more software or firmware programs, a combinationallogic circuit, and/or other suitable components that provide thedescribed functionality.

Embodiments of the present disclosure may be described herein in termsof functional and/or logical block components and various processingsteps. It should be appreciated that such block components may berealized by any number of hardware, software, and/or firmware componentsconfigured to perform the specified functions. For example, anembodiment of the present disclosure may employ various integratedcircuit components, e.g., memory elements, digital signal processingelements, logic elements, look-up tables, or the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. In addition, those skilled inthe art will appreciate that embodiments of the present disclosure maybe practiced in conjunction with any number of systems, and that thesystems described herein is merely exemplary embodiments of the presentdisclosure.

For the sake of brevity, conventional techniques related to signalprocessing, data transmission, signaling, control, and other functionalaspects of the systems (and the individual operating components of thesystems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in an embodiment of the present disclosure.

Referring to FIG. 1 , a functional block diagram representation of anautonomous vehicle communicatively coupled to an edge computing system(ECS) including an embodiment of a traffic light region of interest(ROI) management system is shown. The autonomous vehicle 100 generallyincludes a chassis 112, a body 114, front wheels 116, and rear wheels118. The body 114 is arranged on the chassis 112 and substantiallyencloses components of the autonomous vehicle 100. The body 114 and thechassis 112 may jointly form a frame. The front wheels 116 and the rearwheels 118 are each rotationally coupled to the chassis 112 near arespective corner of the body 114.

The autonomous vehicle 100 is, for example, a vehicle that isautomatically controlled to carry passengers from one location toanother. While the autonomous vehicle 100 is depicted in the illustratedembodiment as a passenger car, other examples of autonomous vehiclesinclude, but are not limited to, motorcycles, trucks, sport utilityvehicles (SUVs), recreational vehicles (RVs), marine vessels, andaircraft. In an embodiment, the autonomous vehicle 100 is a so-calledLevel Four or Level Five automation system. A Level Four systemindicates “high automation”, referring to the driving mode-specificperformance by an automated driving system (ADS) of all aspects of thedynamic driving task, even if a human driver does not respondappropriately to a request to intervene. A Level Five system indicates“full automation”, referring to the full-time performance by an ADS ofall aspects of the dynamic driving task under all roadway andenvironmental conditions that can be managed by a human driver.

As shown, the autonomous vehicle 100 generally includes a propulsionsystem 120, a transmission system 122, a steering system 124, a brakesystem 126, a vehicle sensor system 128, an actuator system 130, atleast one data storage device 132, at least one controller 134, and avehicle communication system 136. The propulsion system 120 may, invarious embodiments, include an internal combustion engine, an electricmachine such as a traction motor, and/or a fuel cell propulsion system.The transmission system 122 is configured to transmit power from thepropulsion system 120 to the front wheels 116 and the rear wheels 118according to selectable speed ratios. According to various embodiments,the transmission system 122 may include a step-ratio automatictransmission, a continuously-variable transmission, or other appropriatetransmission. The brake system 126 is configured to provide brakingtorque to the front wheels 116 and the rear wheels 118. The brake system126 may, in various embodiments, include friction brakes, brake by wire,a regenerative braking system such as an electric machine, and/or otherappropriate braking systems. The steering system 124 influences aposition of the front wheels 116 and the rear wheels 118. While depictedas including a steering wheel for illustrative purposes, in someembodiments contemplated within the scope of the present disclosure, thesteering system 124 may not include a steering wheel.

The vehicle sensor system 128 includes one or more vehicle sensingdevices 140 a-140 n that sense observable conditions of the exteriorenvironment and/or the interior environment of the autonomous vehicle100. Examples of vehicle sensing devices 140 a-140 n include, but arenot limited to, radars, lidars, global positioning systems, opticalcameras, thermal cameras, ultrasonic sensors, and/or other sensors. Theactuator system 130 includes one or more actuator devices 142 a-142 nthat control one or more vehicle features such as for example, but notlimited to, the propulsion system 120, the transmission system 122, thesteering system 124, and the brake system 126. In various embodiments,the vehicle features can further include interior and/or exteriorvehicle features such as for example, but are not limited to, doors, atrunk, and cabin features such as for example air, music, and lighting.

The vehicle communication system 136 is configured to wirelesslycommunicate information to and from other entities(“vehicle-to-everything (V2X)” communication). For example, the vehiclecommunication system 136 is configured to wireless communicateinformation to and from other vehicles 148 (“vehicle-to-vehicle (V2V)”communication), to and from driving system infrastructure (“vehicle toinfrastructure (V2I)” communication), remote systems, to and from an ECS150 and/or personal devices. In an embodiment, the vehicle communicationsystem 136 is a wireless communication system configured to communicatevia a wireless local area network (WLAN) using IEEE 802.11 standards orby using cellular data communication. However, additional or alternatecommunication methods, such as a dedicated short-range communications(DSRC) channel, are also considered within the scope of the presentdisclosure. DSRC channels refer to one-way or two-way short-range tomedium-range wireless communication channels designed for automotive useand a corresponding set of protocols and standards.

The data storage device 132 stores data for use in automaticallycontrolling the autonomous vehicle 100. The data storage device 132 maybe part of the controller 134, separate from the controller 134, or partof the controller 134 and part of a separate system.

The controller 134 includes at least one processor 144 and a computerreadable storage device 146. The computer readable storage device 146may also be referred to a computer readable media 146 and a computerreadable medium 146. The processor 144 can be any custom made orcommercially available processor, a central processing unit (CPU), agraphics processing unit (GPU), an auxiliary processor among severalprocessors associated with the controller 134, a semiconductor-basedmicroprocessor (in the form of a microchip or chip set), amacroprocessor, any combination thereof, or generally any device forexecuting instructions. The computer readable storage device 146 mayinclude volatile and nonvolatile storage in read-only memory (ROM),random-access memory (RAM), and keep-alive memory (KAM), for example.KAM is a persistent or non-volatile memory that may be used to storevarious operating variables while the processor 144 is powered down. Thecomputer-readable storage device 146 may be implemented using any of anumber of known memory devices such as PROMs (programmable read-onlymemory), EPROMs (electrically PROM), EEPROMs (electrically erasablePROM), flash memory, or any other electric, magnetic, optical, orcombination memory devices capable of storing data, some of whichrepresent executable instructions, used by the controller 134 incontrolling the autonomous vehicle 100.

The instructions may include one or more separate programs, each ofwhich comprises an ordered listing of executable instructions forimplementing logical functions. The instructions, when executed by theprocessor 144, receive and process signals from the vehicle sensorsystem 128, perform logic, calculations, methods and/or algorithms forautomatically controlling the components of the autonomous vehicle 100,and generate control signals to the actuator system 130 to automaticallycontrol one or more components of the autonomous vehicle 100 based onthe logic, calculations, methods, and/or algorithms. Although only onecontroller 134 is shown in FIG. 1 , alternative embodiments of theautonomous vehicle 100 can include any number of controllers 134 thatcommunicate over any suitable communication medium or a combination ofcommunication mediums and that cooperate to process the sensor signals,perform logic, calculations, methods, and/or algorithms, and generatecontrol signals to automatically control features of the autonomousvehicle 100.

In various embodiments, one or more instructions of the controller 134are embodied to provide ADS functions as described with reference to oneor more of the embodiments herein. The controller 134 or one of itsfunctional modules is configured to implement the functions described inaccordance with traffic light ROI parameters received from embodimentsof a traffic light ROI management system 152 at an ECS 150.

Referring to FIG. 2 , a diagrammatic representation of an autonomousvehicle 100 capturing a sequence of images including traffic lights 200a, 200 b for transmission to an ECS 150 including an embodiment of atraffic light ROI management system 152 is shown. The autonomous vehicle100 includes a vehicle sensor system 128 and a vehicle communicationsystem 136. The vehicle sensor system 128 includes one or more vehiclesensing devices 140 a-140 n. Examples of vehicle sensing devices 140a-140 n include, but are not limited to, radars, lidars, globalpositioning system (GPS), optical cameras, thermal cameras, ultrasonicsensors, and/or other sensors.

The vehicle sensor system 128 is configured to capture a sequence ofimages as the autonomous vehicle 100 approaches the traffic lights 200a, 200 b on a road segment 202. Each image in the sequence of imagesincludes the traffic lights 200 a, 200 b. The vehicle sensor system 128is configured to identify a vehicle location of the autonomous vehicle100 at the time each image is captured by the vehicle sensor system 128and associate the vehicle location with the image. The images in thesequence of images are in a forward time order. The images are arrangedin the order that the images were captured by the vehicle sensor system128. For example, the first image in the sequence of images is theoldest image in the sequence and the last image in the sequence ofimages is the more recent image in the sequence. The vehiclecommunication system 136 is configured to transmit the sequence ofimages and the vehicle location associated with each of the images tothe traffic light ROI management system 152 at the ECS 150. While twotraffic lights 200 a, 200 b are shown on the road segment 202, the roadsegment 202 may include a fewer or greater number of traffic lights.

Referring to FIG. 3 , a diagrammatic representation of a plurality ofautonomous vehicles 100 communicatively coupled to an ECS 150 includingan embodiment of a traffic light ROI management system 152 is shown.Each of a plurality of autonomous vehicles 100 in a group 300 isconfigured to capture a sequence of images as the autonomous vehicle 100approaches the traffic lights 200 a, 200 b on a road segment 202 fortransmission to an ECS 150 including an embodiment of a traffic lightROI management system 152. Each of the autonomous vehicles 100 isconfigured to transmit the sequence of images and a vehicle associatedwith each of the images to the traffic light ROI management system 152at the ECS 150. While the number of autonomous vehicles 100 in the group300 is shown as five autonomous vehicles 100, the group 300 may includea greater or fewer number of autonomous vehicles 100.

The traffic light ROI management system 152 is configured to receive thesequence of images from each of the autonomous vehicles 100 in the group300. The traffic light ROI management system 152 is configured toprocess each sequence of images individually. The traffic light ROImanagement system 152 is configured to perform reverse object (trafficlight) detection and tracking on each of the sequence of images togenerate a ECS traffic light ROI for each of the images in the sequenceof images including traffic lights that exceed a traffic light sizethreshold.

In an embodiment, the traffic light ROI management system 152 isconfigured to generate ROI scaling ratios associated with each of thevehicle locations of the images based on a movement of the ECS trafficlight ROI in the sequence of images. The traffic light ROI managementsystem 152 is configured to determine ECS ROI offsets of the ECS trafficlight ROI in consecutive pairs of images in the sequence of images. EachROI offset is based on a movement of the ECS traffic light ROI in theconsecutive pairs of images. Each ROI offset represents movement of theposition of ECS traffic light ROI from a position in the first one ofthe pair of consecutive images to a position in the second one of thepair of consecutive images. The movement of the ECS traffic light ROI asa function of the vehicle location on the road segment 202 defines thetopology of the road segment 202. The traffic light ROI managementsystem 152 is configured to generate a ROI scaling ratio for each of thevehicle locations associated with each of the images in the sequence ofimages based on a relationship between each of the ROI offsets and thevehicle location associated with the ROI offset.

The traffic light ROI management system 152 is configured to map eachROI scaling ratio to a vehicle location on a high definition (HD) mapincluding the road segment 202. The vehicle location on the HD map isassociated with the ROI scaling ratio. The HD map is stored at an ECSdatabase 304. The traffic light ROI management system 152 is configuredto repeat this process with each of the sequence of images received fromthe autonomous vehicles 100 in the group 300 to define, refine and/orupdate the ROI scaling ratio for each of a plurality of vehiclelocations on the road segment 202. In an embodiment, the traffic lightROI management system 152 is configured to only generate ROI scalingratios for vehicle locations associated images in the sequence of imagesthat include traffic lights that exceed the traffic light sizethreshold.

In an embodiment, the traffic ROI management system 152 is configured toreceive the ECS traffic light ROIs and the vehicle locations associatedwith each of the images in the image sequence that include the ECStraffic light ROIs. The traffic light ROI management system 152 isconfigured to generate a visual feature template for each of the ECStraffic light ROI. In an embodiment, the traffic ROI management system152 is configured to generate the visual feature template for each ofthe ECS traffic light ROI in the frequency domain by applying a FastFourier transform to the ECS traffic light ROI.

The traffic light ROI management system 152 is configured to map eachvisual feature template to a vehicle location on a high definition (HD)map including the road segment 202. The vehicle location on the HD mapis associated with the image in the image sequence that includes the ECStraffic light ROI associated with the visual feature template. The HDmap is stored at the ECS database 304. The traffic light ROI managementsystem 152 is configured to repeat this process with each of thesequence of images received from the autonomous vehicles 100 in thegroup 300 to define, refine and/or update the visual feature templatefor each of a plurality of vehicle locations on the road segment 202. Inan embodiment, the traffic light ROI management system 152 is configuredto only generate visual feature templates for vehicle locationsassociated images in the sequence of images that include traffic lightsthat exceed the traffic light size threshold.

In an embodiment, the traffic light ROI management system 152 isconfigured to identity the ECS traffic light ROI that include robustvisual features. Each of the ECS traffic light ROI identified as havingrobust visual features are based on an image associated with a vehiclelocation. In an embodiment, the identified ECS traffic light ROI areassociated with the vehicle location on the road segment 202 associatedwith the image that the ECS traffic light ROI is based on. In anembodiment, the identified ECS traffic light ROI may be associated withmultiple vehicle locations on the road segment 202.

The traffic light ROI management system 152 is configured to transmitthe HD map to an autonomous vehicle 100, 302. The HD map includes theROI scaling ratios and visual feature templates mapped to differentvehicle locations on the road segment 202. The ROI scaling ratios andthe visual feature templates may be referred to as traffic light ROIparameters. In an embodiment, the traffic light ROI management system152 is configured to transmit HD maps including the ROI scaling ratiosand visual feature templates mapped to the vehicle locations on the roadsegments 202 including traffic lights 200 a, 220 b in an area within apredefined vicinity of a location of the autonomous vehicle 100, 302. Inan embodiment, the traffic light ROI management system 152 is configuredto transmit HD maps including the ROI scaling ratios and the visualfeature templates mapped to vehicle locations on road segments 202including traffic lights 200 a, 200 b on a route that the autonomousvehicle 100, 302 is expected to take, for example on a road trip.

The autonomous vehicle 100, 302 includes a vehicle sensor system 128.The vehicle sensor system 128 includes one or more vehicle sensingdevices 140 a-140 n. Examples of vehicle sensing devices 140 a-140 ninclude, but are not limited to, radars, lidars, global positioningsystem (GPS), optical cameras, thermal cameras, ultrasonic sensors,and/or other sensors.

The vehicle sensor system 128 is configured to capture real-time imagesas the autonomous vehicle 100, 302 approaches the traffic lights 200 a,200 b on a road segment 202 and generate a real-time traffic light ROIincluding the traffic lights 200 a, 200 b for each of the capturedreal-time images. An uneven or bumpy topology of the road segment 202may cause the traffic lights 200 a, 200 b to “jump” outside thereal-time traffic light ROI. In an embodiment the autonomous vehicle100, 302 is configured to identity the ROI scaling ratios in the HD mapmapped to the vehicle location of the autonomous vehicle 100, 302 on theroad segment 202 and apply the ROI scaling ratio to the real-time imagecaptured at the vehicle location.

The autonomous vehicle 100, 302 is configured to adjust a size of thereal-time traffic light ROI in accordance with the ROI scaling ratio.Adjusting the size of the real-time traffic light ROI in each of thereal-time images captured by the autonomous vehicle 100, 302 using a ROIscaling ratio may ensure that the traffic lights 200 a, 200 b remainwithin the real-time traffic light ROI during changes in topology of theroad segment 202 leading up to the traffic lights 200 a, 200 b andenable the ADS of the autonomous vehicle 100, 302 to take appropriateaction based on a status of the traffic lights 200 a, 200 b on the roadsegment 202. An onboard traffic light object detection algorithm at theautonomous vehicle 100, 302 is used to process the traffic light ROI todetect the traffic lights in the traffic light ROI.

In an embodiment the autonomous vehicle 100, 302 is configured toidentity the visual feature template in the HD map mapped to the vehiclelocation of the autonomous vehicle 100, 302 on the road segment 202. Theautonomous vehicle 100, 302 is configured to identify a real-timetraffic light ROI in a real-time image captured by the vehicle sensorsystem 128 at the vehicle location based on a match between theidentified visual feature template and the real-time visual featuresassociated with the real-time image. An onboard traffic light objectdetection algorithm at the autonomous vehicle 100, 302 is used toprocess the traffic light ROI to detect the traffic lights in thetraffic light ROI. While the group 302 that receives the HD mapsincluding at least one of the ROI scaling ratios and the visual featuretemplates is shown as a single autonomous vehicle 100, the group 302 mayinclude a greater number of autonomous vehicles 100, 302.

Referring to FIG. 4 , a functional block diagram representation of anECS 150 including an embodiment of a traffic light ROI management system152 shown. The ECS 150 is configured to be communicatively coupled toautonomous vehicles 100 represented by the group 300 to receive thesequences of images and to autonomous vehicles 100 represented by thegroup 302 to transmits HD maps including at least one of ROI scalingratios and visual feature templates associated with different vehiclelocations on road segments 202 including traffic lights 200 a, 200 b.The ROI scaling ratios and the visual feature templates may be referredto as traffic light ROI parameters.

In an embodiment, the ECS 150 includes the ECS traffic light ROImanagement system 152 and a ECS database 304. In an embodiment, the ECStraffic light ROI management system 152 includes one or more processors402, a memory 404 and a ECS database 304. In an embodiment, the memory404 includes a traffic light ROI module 406 and a ROI scaling ratiomodule 408. In an embodiment, the memory 404 includes a traffic lightROI module 406 and a visual feature template module 410. In anembodiment, the memory 404 includes a traffic light ROI module 406, aROI scaling ratio module 408, and a visual feature template module 410.In an embodiment, the memory 404 includes a template selection module412. The ECS 150 may include other components that facilitate theoperation of the ECS 150.

Referring to FIG. 5 , a flowchart representation of an example of amethod 500 of generating a plurality of ECS traffic light ROI based on asequence of images received from an autonomous vehicle 100, 300 using anembodiment of a traffic light ROI management system 152 is shown. Themethod 500 may be performed by hardware circuitry, firmware, software,and/or combinations thereof

At 502, a sequence of images is captured by a vehicle sensor system 128of an autonomous vehicle 100, 300 as the autonomous vehicle 100, 300approaches one or more traffic lights 200 a, 200 b on a road segment202. The images in the sequence of images are arranged in a forward timeorder. The images are arranged in the order that the images werecaptured by the vehicle sensor system 128. For example, the first imagein the sequence of images is the oldest image in the sequence and thelast image in the sequence of images is the more recent image in thesequence.

At 504, the autonomous vehicle 100, 300 associates a vehicle locationwith each of the images in the sequence of images. The vehicle sensorsystem 128 is configured to capture vehicle locations of the autonomousvehicle 100, 300 as the autonomous vehicle 100, 300 travels on the roadsegment 202 towards the traffic lights 200 a, 200 b. The vehiclelocation at which each image in the sequence images was captured by thevehicle sensor system 128 is associated with that image. At 506, theautonomous vehicle 100, 300 transmits the sequence of images and thevehicle location associated with each of the images to the traffic lightROI management system 152 at the ECS 150.

The traffic light ROI management system 152 applies a reverse objectdetection and tracking algorithm to each of the images the sequence ofimages in a reverse time order. In a reverse time order of the images,the first image is the most recent image in the sequence and the lastimage is the oldest image in the sequence. At 508, the traffic light ROImodule 406 receives an image from the reverse time ordered sequence ofimages.

At 510, the traffic light ROI module 406 uses an object trackingalgorithm to detect the traffic lights 200 a, 200 b in the image. In anembodiment, each detected traffic light 200 a, 200 b in the image isrepresented as bounding box. At 512, the traffic light ROI module 406determines whether the size of each of the detected traffic lights 200a, 200 b represented as bounding boxes in the image is greater than atraffic light size threshold.

If the traffic light ROI module 406 determines that the size of each ofthe detected traffic lights 200 a, 200 b represented as bounding boxesin the image is not greater than the traffic light size threshold, thetraffic light ROI module 406 discards the image at 514. At 516, thetraffic light ROI module 406 determines whether there are anyunprocessed images remaining in the sequence of images. If ECS trafficlight ROI module 406 determines that there are unprocessed imagesremaining in the sequence of images the method 500 returns to 508 andthe next image in the reverse time ordered image sequence is received bythe traffic light ROI module 406 for processing. If the traffic lightROI module 406 determines that there are no unprocessed images remainingin the sequence of images the method 500 ends at 518.

If the traffic light ROI module 406 determines that the size of each ofthe detected traffic lights 200 a, 200 b represented as bounding boxesin the image is greater than the traffic light size threshold, thetraffic light ROI module 406 generates an ECS traffic light ROI thatencloses the detected traffic lights 200 a, 200 b represented asbounding boxes in the image at 520. At 522, the traffic light ROI module406 associates the vehicle location associated with the image with theECS traffic light ROI. The method 500 proceeds to 516.

At 516, the traffic light ROI module 406 determines whether there areany unprocessed images remaining in the sequence of images. If thetraffic light ROI module 406 determines that there are unprocessedimages remaining in the sequence of images the method 500 returns to 508and the next image in the reverse time ordered image sequence isreceived by the traffic light ROI module 406 for processing. If thetraffic light ROI module 406 determines that there are no unprocessedimages remaining in the sequence of images the method 500 ends at 518.

The method 500 generates a plurality of traffic light ROI based on asequence of images received from an autonomous vehicle 100, 300 as anoutput. The traffic light ROI management system 152 uses the pluralityof traffic light ROI to generate traffic light ROI parameters. In anembodiment, the traffic light ROI parameters are ROI scaling ratios. Inan embodiment, the traffic light ROI parameters are visual featuretemplates. In an embodiment the traffic light ROI parameters includeboth ROI scaling ratios and visual feature templates.

Referring to FIG. 6 a and FIG. 6 b , block diagram representations ofexamples of ECS traffic light ROI positions in a pair of consecutiveimages 600 a, 600 b in the sequence of images is shown. In anembodiment, the ROI scaling ratio module 408 receives the sequence ofimages and the plurality of ECS traffic light ROI based on the sequenceof images. The ROI scaling ratio module 408 is configured to generateROI scaling ratios based on a movement of the ECS traffic light ROI inthe sequence of images.

The first image 600 a includes a first ECS traffic light ROI 602 a andthe second image 600 b includes a second ECS traffic light ROI 602 b.The first and second images 600 a, 600 b are the same size. The firstand second images 600 a, 600 b may be defined by an x, y coordinatesystem, where the x axis runs along a width of each of the first andsecond images 600 a, 600 b and the y-axis runs along a height of each ofthe first and second images 600 a, 600 b. The first ECS traffic lightROI 602 a and the second ECS traffic light ROI 602 b may be differentsizes.

In the example, a position of a center point 604 a of the first ECStraffic light ROI 602 a is represented by the coordinates (x_(i),y_(i)). A position of a center point 604 b of the second ECS trafficlight ROI 602 b is represented by the coordinates (x_(i+i), y_(i+i)).The distance d_(i) represents the movement of the ECS traffic light ROIin consecutive images. In the example, the distance d_(i) represents themovement of the position of the center point 604 a at (x_(i), y_(i)) tothe position of the center point 604 b at (x_(i+i), y_(i+i)). Thedistance d_(i) can be referred to as the ROI offset or the ROI movement.The distance d_(i) is defined as a function of the movement of thepositions of the center points 604 a, 604 b of the first and second ECStraffic light ROIs 602 a, 602 b in the first and second images 600 a,600 b as shown below.

d _(i)=distance ((x _(i) , y _(i)), (x _(i+1) , y _(i+1)))

In the example, the first image 600 a was captured at a first vehiclelocation p_(i) and the second image 600 b was captured at a secondvehicle location p_(i+1). The distance d_(i) is associated with theposition p_(i). The distance d_(i=1) is associated with the positionp_(i=1). The distance d with respect to the corresponding position p iscalculated for each of the consecutive pairs of images in the imagesequence. An example of a graph that may represent the relationshipbetween the distance d (also referred to as the ROI offset or themovement of the ECS traffic light ROI) as a function of vehicle positionp is shown below.

The ROI offset as a function of the vehicle location on a road segment202 defines a topology of the road segment 202. For example, as shown inthe graph, a relatively larger value of the ROI offset indicates that aposition of a bump present in the road segment 202. The ROI scalingratio module 408 is configured to generate a ROI scaling ratio for eachof the vehicle locations associated with each of the images in thesequence of images including a ECS traffic light ROI based on the ROIoffset associated with that vehicle location. The ROI scaling ratio is atraffic ROI parameter. The ROI scaling ratio module 408 is configured tomap the ROI scaling ratio associated with the different vehiclelocations on the road segment 202 to a HD map including the road segment202. The HD map including the mapped ROI scaling ratios are stored inthe ECS database 304.

In an embodiment, the visual feature template module 410 is configuredto receive the ECS traffic light ROIs and the vehicle locationsassociated with each of the images in the image sequence that includethe ECS traffic light ROIs. The visual feature template module 410 isconfigured to generate a visual feature template for each of the ECStraffic light ROI. In an embodiment, the visual feature template module410 is configured to generate the visual feature template for each ofthe ECS traffic light ROI in the frequency domain by applying a FastFourier transform to the ECS traffic light ROI.

The visual feature template module 410 is configured to map each visualfeature template to a vehicle location on a high definition (HD) mapincluding the road segment 202. The vehicle location on the HD map isassociated with the image in the image sequence that includes the ECStraffic light ROI associated with the visual feature template. The HDmap is stored at the ECS database 304. The visual feature templatemodule 410 is configured to repeat this process with each of thesequence of images received from the autonomous vehicles 100 in thegroup 300 to define, refine and/or update the visual feature templatefor each of a plurality of vehicle locations on the road segment 202. Inan embodiment, the visual feature template module 410 is configured toonly generate visual feature templates for vehicle locations associatedimages in the sequence of images that include traffic lights that exceedthe traffic light size threshold.

In an embodiment, the template selection module 412 is configured toreceive the sets of visual feature templates and the sequences of imagesreceived from different autonomous vehicles 100, 300 that previouslytravelled on the road segment 202 towards the traffic lights 200 a, 200b. The template selection module 412 is configured to generate a reducedset of visual feature templates that can be used by an autonomousvehicle 100, 302 to identify real-time traffic light ROIs in real-timeimages captured by autonomous vehicles 100, 302 that travel on the roadsegment 202 towards the traffic lights 200 a, 200 b. The use of areduced set of visual feature templates may reduce the footprint of thevisual feature templates associated with a road segment 202 and mayreduce memory needed to store the visual feature templates.

Referring to FIG. 7 , a diagrammatic representation of an example ofassociations between each of plurality of visual feature templates and aplurality of vehicle locations on a road segment 202 including trafficlights 200 a, 200 b is shown. The vehicle locations are represented ascircles on the road segment 202. The template selection module 412 isconfigured to select a subset of the visual feature templates generatedby the visual feature template module 410 to map to the HD map.

In an embodiment, each visual feature template is a frequency domainrepresentation of the ECS traffic light ROI associated with an imagepreviously captured by an autonomous vehicle 100, 300 at a vehiclelocation on the road segment 202. The template selection module 412 isconfigured to use the frequency domain representation of each of theimages in the sequences of images received from autonomous vehicles 100,300 on the road segment 202 to predict the ECS traffic light ROI ofimages captured in nearby vehicle locations and identify the visualfeature templates that can be used at multiple vehicle locations on theroad segment 202.

For example, in FIG. 7 , the visual feature template V_(i) can be usedto correctly determine a real-time traffic light ROI at each of thevehicle locations represented by the black dots, the visual featuretemplate V_(j) can be used to correctly determine a real-time trafficlight ROI in each of the vehicle locations represented by the gray dotsand V_(k) can be used to correctly determine a real-time traffic lightROI in each of the vehicle locations represented by the white dots.

The set of visual feature templates generated by the visual featuretemplate module 410 for the road segment 202 may, for example, includethirteen different visual feature templates. The template selectionmodule 412 identified a reduced set of the visual feature templatesV_(i), V_(j), V_(k) that can be used to determine real-time trafficlight ROI in real-time images captured by future autonomous vehicles100, 302 at the potential vehicle locations represented by the circleson a road segment 202. Each of the visual feature templates in thereduced set is mapped to the vehicle locations on the road segment 202where the visual feature template can be used to determine real-timetraffic light ROIs.

Referring to FIG. 8 , a diagrammatic representation of an example of avehicle location 800 of an autonomous vehicle 100 and vehicle locationsassociated with a plurality of visual feature templates V_(i), V_(j),V_(k) on a road segment 202 including traffic lights 200 a, 200 b isshown. The vehicle locations are represented as circles on the roadsegment 202. In the example, the visual feature template V_(i) can beused to correctly determine a real-time traffic light ROI at the vehiclelocation represented by the black dot, the visual feature template V_(j)can be used to correctly determine a real-time traffic light ROI at thevehicle location represented by the gray dot, and V_(k) can be used tocorrectly determine a real-time traffic light ROI at the vehiclelocation represented by the white dot. The autonomous vehicle 100 isdisposed at a vehicle location 800 represented by the square. The HD mapdoes not include a visual feature template associated with the vehiclelocation 800.

The autonomous vehicle 100 is configured to run a visual featuretemplate scaling algorithm in real-time. The visual feature templatescaling algorithm calculates a visual feature template scaling ratiousing a size ROISize_(vehicle) of the traffic light ROI at theautonomous vehicle 100 and a size ROISize_(Vj) of a visual featuretemplate V_(j) associated with another vehicle location on the roadsegment 202 within an area represented by an observation angle 802associated with the vehicle location 800 of the autonomous vehicle 100.The visual feature template V_(j) is in the frequency domain. The visualfeature template scaling ratio is calculated using the equation below.

${{Visual}{feature}{template}{scaling}{ratio}} = \frac{{ROISize}_{vehicle}}{{ROISize}_{V_{j}}}$

The visual feature template scaling algorithm applies the visual featuretemplate scaling ratio to the visual feature template V_(j) to generatea scaled version of the visual feature template V_(j). The autonomousvehicle 100 uses the scaled version of the visual feature template V_(j)to identify the real-time traffic light ROI in a real-time imagecaptured by the autonomous vehicle 100 at the vehicle location 800.

Referring to FIG. 9 , an example of a method 900 of providing trafficlight ROI parameters to an autonomous vehicle using an embodiment of atraffic light ROI management system 152 at an ECS 150 is shown. Themethod 900 is performed by the traffic light ROI management system 152.The method 1000 may be performed by the traffic light ROI managementsystem 152 in combination with other components of the ECS 150. Themethod 900 may be performed by hardware circuitry, firmware, software,and/or combinations thereof.

At 902, a sequence of images and a vehicle location associated with eachof the images is received from a first autonomous vehicle 100, 300 at atraffic light ROI management system 152. At 904, at least one trafficlight 200 a, 200 b is detected in each image in the sequence of imagesat the traffic light ROI management system 152. At 906, a ECS trafficlight ROI for each image in the sequence of images is defined at thetraffic light ROI management system 152. The ECS traffic light ROI foreach image encloses the at least one traffic light 202 a, 202 b in theimage. At 908, a visual feature template is generated for each image inthe sequence of images at the traffic light ROI management system 152.The visual feature template for each image is based on the ECS trafficlight ROI for the image. At 910, each visual feature template for eachimage in the sequence of images is mapped to the vehicle locationassociated with the image to a high definition (HD) map at the trafficlight ROI management system 152. At 912, the HD map is transmitted fromthe traffic light ROI management system 152 to a second autonomousvehicle 100, 302 to enable the second autonomous vehicle 100, 302 toidentify a real-time traffic light ROI in a real-time image based on amatch between a first visual feature template of the HD map andreal-time visual features associated with the real-time image at thevehicle location associated with the first visual feature template. Anonboard traffic light object detection algorithm at the autonomousvehicle 100 is used to process the traffic light ROI to detect thetraffic lights in the traffic light ROI.

The use of a traffic light ROI management system 152 at an edgecomputing system 150 may reduce computer resource usage at autonomousvehicles 100. The use of crowd-source vehicle sensor data to createintersection models and traffic light models may reduce computerresource usage at autonomous vehicles 100. The generation of trafficlight ROI parameters at the edge computing system 150 based oncrowd-source vehicle sensor data for use by autonomous vehicles 100 inreal-time may enable autonomous vehicles 100 to identify real-timetraffic light ROIs in real-time images with greater precision andcompensate for complex road topologies.

Processing a full-resolution image in an autonomous vehicle on-boardtraffic light detection algorithm may make the algorithm very slow torun. Extracting a smaller sub-image (Region of Interest) from thefull-resolution image which includes traffic lights in this sub-image,prior to running the traffic light object detection algorithm mayaddress this problem. Extracting a small sub-image (ROI) before runningthe full complex traffic light detection algorithms may effectivelyreduce the response time of the real-time traffic light detectionalgorithms.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. It is tobe understood that various changes can be made in the function andarrangement of elements without departing from the scope of thedisclosure as set forth in the appended claims and the legal equivalentsthereof.

What is claimed is:
 1. An edge computing system (ECS) for providingtraffic light region of interest (ROI) parameters to an autonomousvehicle comprising: a processor; and a memory, the memory includinginstructions that upon execution by the processor cause the processorto: receive a sequence of images and a vehicle location associated witheach of the images from a first autonomous vehicle; detect at least onetraffic light in each image in the sequence of images; define a ECStraffic light ROI for each image in the sequence of images, the ECStraffic light ROI for each image enclosing the at least one trafficlight in the image; generate a visual feature template for each image inthe sequence of images, the visual feature template for each image beingbased on the ECS traffic light ROI for the image; map each visualfeature template for each image in the sequence of images to the vehiclelocation associated with the image to a high definition (HD) map; andtransmit the HD map to a second autonomous vehicle to enable the secondautonomous vehicle to identify a real-time traffic light ROI in areal-time image based on a match between a first visual feature templateof the HD map and real-time visual features associated with thereal-time image at the vehicle location associated with the first visualfeature template.
 2. The system of claim 1, wherein each of the imagesin the sequence of images is in a forward time order and the memoryfurther includes instructions that upon execution by the processor causethe processor to define the ECS traffic light ROI in each image in thesequence of images in a reverse time order.
 3. The system of claim 2,wherein the memory further includes instructions that upon execution bythe processor cause the processor to discard a first image of thesequence of images upon a determination that the at least one detectedtraffic light in first image falls below a traffic light size threshold.4. The system of claim 1, wherein the memory further includesinstructions that upon execution by the processor cause the processor togenerate the visual feature template for each image in the sequence ofimages in the frequency domain by applying a Fast Fourier transform tothe ECS traffic light ROI of the image.
 5. The system of claim 1,wherein the memory further includes instructions that upon execution bythe processor cause the processor to: select a second visual featuretemplate based on a first image of the sequence of images, the firstimage being associated with a first vehicle location; determine whetherthe second visual feature template can be used to identify a first ECStraffic light ROI in a second image of the sequence of images based on amatch between the second visual feature template and visual featuresassociated with the second image, the second image being associated witha second vehicle location; and map the second visual feature template tothe first and second vehicle locations on the HD map based on thedetermination.
 6. The system of claim 1, wherein the memory furtherincludes instructions that upon execution by the processor cause theprocessor to: determine ROI offsets of the ECS traffic light ROI inconsecutive pairs of images in the sequence of images based on amovement of the ECS traffic light ROI in the consecutive pairs ofimages; generate a ROI scaling ratio for each of the vehicle locationsassociated with each of the images in the sequence of images based arelationship between each of the ROI offsets and the vehicle locationassociated with the ROI offset; and map each ROI scaling ratio to thevehicle location on the HD map associated with the ROI scaling ratio. 7.The system of claim 6, wherein the memory further includes instructionsthat upon execution by the processor cause the processor to transmit theHD map including the mapped ROI scaling ratios to the second autonomousvehicle to enable the second autonomous vehicle to adjust a size of thereal time traffic light ROI in the real-time image in accordance withthe ROI scaling ratio associated with the vehicle location of the secondautonomous vehicle.
 8. A computer readable medium comprisinginstructions stored thereon for providing traffic light region ofinterest (ROI) parameters to an autonomous vehicle, that upon executionby a processor, cause the processor to: receive a sequence of images anda vehicle location associated with each of the images from a firstautonomous vehicle; detect at least one traffic light in each image inthe sequence of images; define a ECS traffic light ROI for each image inthe sequence of images, the ECS traffic light ROI for each imageenclosing the at least one traffic light in the image; generate a visualfeature template for each image in the sequence of images, the visualfeature template for each image being based on the ECS traffic light ROIfor the image; map each visual feature template for each image in thesequence of images to the vehicle location associated with the image toa high definition (HD) map; and transmit the HD map to a secondautonomous vehicle to enable the second autonomous vehicle to identify areal-time traffic light ROI in a real-time image based on a matchbetween a first visual feature template of the HD map and real-timevisual features associated with the real-time image at the vehiclelocation associated with the first visual feature template.
 9. Thecomputer readable medium of claim 8, further comprising instructions tocause the processor to: receive the sequence of images in a forward timeorder; and define the ECS traffic light ROI in each image in thesequence of images in a reverse time order.
 10. The computer readablemedium of claim 9, further comprising instructions to cause theprocessor to discard a first image of the sequence of images upon adetermination that the at least one detected traffic light in firstimage falls below a traffic light size threshold.
 11. The computerreadable medium of claim 8, further comprising instructions to cause theprocessor to generate the visual feature template for each image in thesequence of images in the frequency domain by applying a Fast Fouriertransform to the ECS traffic light ROI of the image.
 12. The computerreadable medium of claim 9, further comprising instructions to cause theprocessor to: select a second visual feature template based on a firstimage of the sequence of images, the first image being associated with afirst vehicle location; determine whether the second visual featuretemplate can be used to identify a first ECS traffic light ROI in asecond image of the sequence of images based on a match between thesecond visual feature template and visual features associated with thesecond image, the second image being associated with a second vehiclelocation; and map the second visual feature template to the first andsecond vehicle locations on the HD map based on the determination. 13.The computer readable medium of claim 9, further comprising instructionsto cause the processor to: determine ROI offsets of the ECS trafficlight ROI in consecutive pairs of images in the sequence of images basedon a movement of the ECS traffic light ROI in the consecutive pairs ofimages; generate a ROI scaling ratio for each of the vehicle locationsassociated with each of the images in the sequence of images based arelationship between each of the ROI offsets and the vehicle locationassociated with the ROI offset; and map each ROI scaling ratio to thevehicle location on the HD map associated with the ROI scaling ratio.14. The computer readable medium of claim 9, further comprisinginstructions to cause the processor to transmit the HD map including themapped ROI scaling ratios to the second autonomous vehicle to enable thesecond autonomous vehicle to adjust a size of the real time trafficlight ROI in the real-time image in accordance with the ROI scalingratio associated with the vehicle location of the second autonomousvehicle.
 15. A method of providing traffic light region of interest(ROI) parameters to an autonomous vehicle comprising: receiving asequence of images and a vehicle location associated with each of theimages from a first autonomous vehicle at a traffic light ROI managementsystem; detecting at least one traffic light in each image in thesequence of images at the traffic light ROI management system; defininga ECS traffic light ROI for each image in the sequence of images at thetraffic light ROI management system, the ECS traffic light ROI for eachimage enclosing the at least one traffic light in the image; generatinga visual feature template for each image in the sequence of images atthe traffic light ROI management system, the visual feature template foreach image being based on the ECS traffic light ROI for the image;mapping each visual feature template for each image in the sequence ofimages to the vehicle location associated with the image to a highdefinition (HD) map at the traffic light ROI management system; andtransmitting the HD map from the traffic light ROI management system toa second autonomous vehicle to enable the second autonomous vehicle toidentify a real-time traffic light ROI in a real-time image based on amatch between a first visual feature template of the HD map andreal-time visual features associated with the real-time image at thevehicle location associated with the first visual feature template. 16.The method of claim 15, further comprising: receiving the sequence ofimages in a forward time order at the traffic light ROI managementsystem; and defining the ECS traffic light ROI in each image in thesequence of images in a reverse time order at the traffic light ROImanagement system.
 17. The method of claim 16, further comprisingdiscarding a first image of the sequence of images upon a determinationthat the at least one detected traffic light in first image falls belowa traffic light size threshold at the traffic light ROI managementsystem.
 18. The method of claim 15, further comprising generating thevisual feature template for each image in the sequence of images in thefrequency domain by applying a Fast Fourier transform to the ECS trafficlight ROI of the image at the traffic light ROI management system. 19.The method of claim 15, further comprising: selecting a second visualfeature template based on a first image of the sequence of images at thetraffic light ROI management system, the first image being associatedwith a first vehicle location; determining whether the second visualfeature template can be used to identify a first ECS traffic light ROIin a second image of the sequence of images based on a match between thesecond visual feature template and visual features associated with thesecond image at the traffic light ROI management system, the secondimage being associated with a second vehicle location; and mapping thesecond visual feature template to the first and second vehicle locationson the HD map based on the determination at the traffic light ROImanagement system.
 20. The method of claim 15, further comprising:determining ROI offsets of the ECS traffic light ROI in consecutivepairs of images in the sequence of images based on a movement of the ECStraffic light ROI in the consecutive pairs of images at the trafficlight ROI management system; generating a ROI scaling ratio for each ofthe vehicle locations associated with each of the images in the sequenceof images based a relationship between each of the ROI offsets and thevehicle location associated with the ROI offset at the traffic light ROImanagement system; and mapping each ROI scaling ratio to the vehiclelocation on the HD map associated with the ROI scaling ratio at thetraffic light ROI management system.